Abstract and Demos for "'You're my doctor?': Stereotype-incongruent identities impair recognition of incidental visual features"

Austin Baker, Jorge Morales & Chaz Firestone


Social stereotypes shape our judgments about people around us. What types of judgments are susceptible to such biased interference? A striking example of stereotype bias involves the treatment of people whose identities run counter to our stereotypes——as when women are assumed to be students, research assistants, or nurses rather than professors, principal investigators, or doctors. Can such stereotypes also intrude on representations that have nothing to do with the content of the stereotype in question? Here, we explore how the assumptions we make about other people can impair our ability to process completely incidental, and surprisingly low-level, aspects of their appearance——including even their location in space. We collected professional headshots of male and female physicians from a major medical institution, and asked subjects simply to indicate the direction of the depicted subject's shoulders (left or right)——an extremely straightforward task that subjects performed with near-ceiling accuracy. The key manipulation was a cue on each trial that the upcoming image would be of a "doctor" or a "nurse", and a regularity in the experiment such that "doctor"-labeled images tended to face one way and "nurse"-labeled images tended to face the other way. Even though the gender of the subjects was completely irrelevant to any aspect of the task, subjects were slower to judge the orientation of stereotype-incongruent people (female "doctors" and male "nurses") than stereotype-congruent people (male "doctors" and female "nurses"), even though the images were identical in both conditions (with only the labels changing), including in a large direct replication. Follow-up experiments without these regularities showed that this effect couldn't be explained by the raw surprisingness of, e.g., seeing a man when expecting a nurse; instead, these results suggest that even straightforward forms of statistical learning (here, between labels and orientations) can be intruded upon by long-held social biases, and in ways that alter processing of incidental and basic visual features.


Experiment 1 & 2

Experiment 3